PL 37, Roadway Inventory Data from Mandli Implementation

KYTC Division of Maintenance collects roadway data using Mandli pavement scanners. These pavement scanners record information at points along the roadway and use the data directly or by interpolation. Mandli scanners provide detailed information that can be used in the calculation of pavement geometry including horizontal curve characteristics, such as superelevation, and vertical curvature (grade). The roadway data is processed by Mandli software. Mandli software can process a large number of datapoints to derive these data items. Two previous research projects have explored the accuracy and usability of Mandli data. The first research project’s conclusion was that Mandli, in its current form, is not entirely accurate. Instead, a Matlab tool (UKCAT) was developed that processes the raw data to give more accurate information. The second research project expanded upon the tool’s development and focused on implementation of the tool on Mandli data. The Matlab tool was modified to work on vertical curves as well as horizontal curves, and thresholds were decided based on feedback from KYTC. Then the tool was run on Mandli research data from each of Kentucky’s 120 counties. The main focus of this second research project was developing a methodology to convert raw Mandli data into usable data. There are ten steps in the research process, including pre-processing in Excel and ArcMap, using a python code to add accurate milepoints, running the Matlab tool, post-processing in Excel and ArcMap to provide a complete picture of both curves and tangents, and performing several quality control measures on the data in its final form. This current research project will continue the research into the implementation of the UKCAT Matlab tool on existing 2017 data, will deliver new research results for 2018 data, and will provide thorough quality control on the research results. The purpose is to compile a record of all identified horizontal and vertical curves, as well as relevant curve parameters such as radius, length, grade, and superelevation. These research records will be presented in shapefiles at project completion, along with a procedure documentation for clarity and repeatability. Overall, the objective of this research is to extract horizontal curve, vertical curve, and superelevation records for the roads functionally classified as collectors or above. To that end, the process is divided into two main sections: Finish the research process on existing 2017 research data: 1) Trouble-shoot the counties that have had issues with the Matlab tool (about 10 of these exist) 2) Post-process the results from the UKCAT tool (about 40 counties remaining) 3) Perform quality control measures and format for KYTC’s use. (135 counties remaining) Roads that were missing from the 2017 research data will be provided in the 2018 format. 4) For 2018 data, perform pre-processing and add milepoints on all 120 counties 5) Run UKCAT Matlab tool 6) Post-process UKCAT results in Excel and use ArcMap to add tangents 7) Perform quality control measures and format for KYTC’s use.